Awesome Journal Skills is a collection of agent skill packs tailored to hundreds of academic journals across fields including economics, social science, medicine, science, and engineering. Researchers use the packs for tasks such as choosing topics, designing empirical strategies, preparing tables and figures, submitting papers, and responding to reviewers. The catalogue entries are the project's journal-specific skills and related plugins.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-camera-readygit clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-camera-ready)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-camera-ready/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-camera-ready.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.00982 |
| Opus 5 | $0.00034 | $0.00491 |
| Sonnet 5 | $0.00013 | $0.00196 |
| Haiku 4.5 | $0.00007 | $0.00098 |
Grade A, and why
acmmm-camera-ready scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACM MM Camera-Ready
Use this after acceptance to turn the reviewed PDF into the ACM Digital Library version of record. Camera-ready is a distinct piece of work with its own deadline (reported August 6 for 2026) and its own failure modes — several of them ACM-specific.
De-anonymization pass
The reviewed paper was anonymous (main track); the camera-ready is not. Reverse the blinding carefully:
- Add author names, affiliations, and acknowledgements/funding.
- Restore "our previous work" phrasing and the real repository/dataset URLs.
- Replace the anonymous data/code mirror with the permanent, public archive (DOI).
- Re-check that no anonymous-only placeholder text survives.
ACM publishing requirements
| Item | What to do |
|---|---|
| ACM rights form | Complete the eRights process; paste the returned rights block and DOI into the paper |
| CCS concepts | Add ACM Computing Classification System concepts and author keywords |
| sigconf compliance | Final PDF in the current ACM sigconf template; pass ACM's PDF/format check |
| Reference/overflow | References may occupy the extra pages; the body stays within the accepted length |
| Metadata | Title, authors, affiliations, and abstract match the ACM submission exactly |
The exact ACM rights/OA options (including ACM Open) are settled here; confirm the current process (待核实 each cycle).
Artifact and badge release
code: public repo + archived DOI, LICENSE, README with run instructions
data: dataset card, license, access path (mirror or agreement)
media: final demo video/audio, now de-anonymized, playable in open players
badge: if the Reproducibility track awarded a badge, include the badge and artifact link
Camera-ready is when the public release replaces the anonymous review artifact — do not leave the community pointing at a dead anonymous link.
Registration and attendance
- At least one author must register by the deadline and present; confirm the current registration category and any presenter requirement.
- Check visa/travel needs for Rio de Janeiro early — international travel is the common logistical failure.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 100 lines · 67 tokens per session scan A 69f777fd01f7
acmmm-camera-ready is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,097 stars, last pushed 16d ago), licensed MIT. It adds 67 tokens to every session and 982 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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thesis-consistency-audit
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A reading tool for translating an English research-paper PDF into Chinese paragraph by paragraph, with color labels for claims, innovations, methods, limits, and quotable sentences. It uses a reusable offline HTML reader and stores highlights in the browser.